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Develop and evaluate a deep artificial neural network to accurately identify hundreds of North American and European bird species from passive acoustic recordings.

January 27, 2021Ecological InformaticsOpen Access

BirdNET: A deep learning solution for avian diversity monitoring

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Develop and evaluate a deep artificial neural network to accurately identify hundreds of North American and European bird species from passive acoustic recordings.
Engineered BirdNET, a 157-layer residual network (ResNet) with over 27 million parameters trained using domain-specific data pre-processing, augmentation, mixup, and short FFT window spectrograms to classify 984 species.
Tested the model across three independent datasets: 22,960 single-species recordings, 286 hours of fully annotated autonomous soundscapes, and 33,670 hours of continuous audio collected over 4 years near four eBird hotspots.
BirdNET achieved a mean average precision of 0.791 for single-species recordings and an F0.5 score of 0.414 on fully annotated soundscapes.
Acoustic predictions yielded an average correlation of 0.251 with expert human observations across 121 species over 4 years of audio data.
High temporal resolution in input spectrograms and domain-specific data augmentation proved critical for resolving overlapping vocalizations and high ambient background noise.

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Algorithm evaluation study demonstrates robust acoustic identification across 984 bird species, highlighting deep learning's power for automated ecological monitoring.

Cite This Study

Kahl et al. (2021) studied this question.

synapsesocial.com/papers/69d8114f617ce96c42ae2d05 https://doi.org/10.1016/j.ecoinf.2021.101236

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Implication

Algorithm evaluation study demonstrates robust acoustic identification across 984 bird species, highlighting deep learning's power for automated ecological monitoring.

BirdNET: A deep learning solution for avian diversity monitoring

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BirdNET: A deep learning solution for avian diversity monitoring

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Implication

Cite This Study

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